Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add fabioc-aloha/Alex_Skill_Mall --skill default-fast-opt-slowgit clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_MallWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/default-fast-opt-slow)<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/default-fast-opt-slow"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/default-fast-opt-slow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/default-fast-opt-slow"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/default-fast-opt-slow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.00457 |
| Opus 5 | $0.00009 | $0.00229 |
| Sonnet 5 | $0.00004 | $0.00091 |
| Haiku 4.5 | $0.00002 | $0.00046 |
Grade A, and why
default-fast-opt-slow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Default to Fast, Opt Into Slow
The Problem
AI/LLM features default to verbose responses, causing:
- Long wait times for simple queries
- Token/cost waste
- User frustration
Users who want depth will ask for it.
The Solution
Default to the shortest useful response length. Let users opt into more.
// Default: concise
const DEFAULT_RESPONSE_LENGTH = 'concise';
// User can select
const responseLengths = {
concise: { maxTokens: 150, instruction: 'Be brief. One paragraph max.' },
standard: { maxTokens: 500, instruction: 'Provide a clear explanation.' },
detailed: { maxTokens: 2000, instruction: 'Explain thoroughly with examples.' }
};
// Persist preference
function getResponseConfig(userId) {
const pref = localStorage.getItem(`response-length-${userId}`);
return responseLengths[pref] || responseLengths[DEFAULT_RESPONSE_LENGTH];
}
UI Pattern
<!-- Simple toggle in UI -->
<select id="response-length">
<option value="concise" selected>Quick (default)</option>
<option value="standard">Standard</option>
<option value="detailed">Detailed</option>
</select>
Persist Preference
Once a user selects "detailed," remember it:
// Save on change
select.addEventListener('change', (e) => {
localStorage.setItem('response-length', e.target.value);
});
// Restore on load
const saved = localStorage.getItem('response-length');
if (saved) select.value = saved;
Verification
- Fresh user gets concise responses
- User can switch to detailed
- Preference persists across sessions
- Response length actually changes
When to Apply
- LLM-powered features
- API responses with variable depth
- Documentation generation
- Any feature where "more" costs time/resources
Tags
architecture ux llm performance
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 84 lines · 18 tokens per session scan A a78716ac6df5
default-fast-opt-slow is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (4 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 457 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
vendor-prompting
ANALYSIS SKILL — Audit-grade reference for Anthropic Claude and OpenAI GPT-5.6 prompting best practices. WHEN: "claude prompting", "gpt-5.6 prompting", "audit agent", "review prompt", "vendor best practices", "anthropic best practices", "openai prompting". DO NOT USE FOR: routine prompt edits where rules are already…
ai-orchestration-langchain
LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing.
ai-observability-promptfoo
Testing and evaluation framework for LLM prompts and applications -- promptfooconfig.yaml, assertions, model-graded evals, red teaming, CI/CD integration, custom providers, and comparative evaluation.
midjourney-prompter
Engineer Midjourney prompts — style references, aspect ratios, negative prompts, and v6 parameter tuning.
stable-diffusion-helper
Craft Stable Diffusion prompts — SDXL, LoRA triggers, ControlNet hints, and ComfyUI workflow design.
model-recommendation
Analyse chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency.